Model predictive control algorithms and their application to a continuous fermenter

In many continuous fermentation processes, the control objective is to maxi mize productivity per unit time. The optimum operational point in the stead y state can be obtained by maximizing the productivity rate using feed subs trate concentration as the independent variable with the equations of the s tatic model as constraints. In the present study, three model-based control schemes have been developed and implemented for a continuous fermenter. Th e first method modifies the well-known dynamic matrix control (DMC) algorit hm by making it adaptive. The other two use nonlinear model predictive cont rol algorithms (NMPC, nonlinear model predictive control) for calculation o f control actions. The NMPC1 algorithm, which uses orthogonal collocation i n finite elements, acted similar to NMPC2, which uses equidistant collocati on. These algorithms are compared with DMC. The results obtained show the g ood performance of nonlinear algorithms.